Aluminum alloy was tested through pre-corrosion in NaCl solution. The corrosion pits were detected to get corrosion damage data of different corrosion time. Corrosion fatigue test of the pre-corroded specimen was carried out. Gray prediction model and BP neural network algorithms were selected to establish predictive model of the relation between fatigue lives, corrosion depth, and corrosion time. The accuracy of both two prediction model were compared. The result showed that the prediction accuracy of gray prediction model is higher than BP neural network algorithm when the statistics is lack.
NETL
NSTL
维普
万方数据
